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20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020

作者： Uhlmann, E. Polte, M. Streckenbach, J. Dinh, N.C. Börnstein, J. Wolf, C.-S. Camin, B. Institute for Machine Tools Factory Management IWF Technische Universität Berlin Germany Fraunhofer Institute for Production Systems and Design Technology IPK Germany Institute for Materials Science and Technology Chair of Metallic Materials Technische Universität Berlin Germany

ISBN: (纸本)9780995775176

The production of components for automotive and aerospace industry by conventional machining is still limited by hardness and strength of the workpiece materials. Electro-discharge drilling is used for machining electrically conductive materials without any limitation due to mechanical properties. Electro-discharge drilling causes debris in the working gap, which leads to arcs and short circuits on the lateral surface with negative effects on processing results and process duration. Due to these arcs and short circuits limited drilling depth, increased tool wear, conicity of boreholes and process instabilities are still challenges in electro-discharge drilling. In this work, a new approach for passivation of the tool electrode material by oxidation is shown. Different oxidation processes for tool electrodes made of brass were applied and analysed. Surface modified tool electrodes were used for electro-discharge drilling of Elmax, SuperClean from the company VOELSTAPINE AG, Linz, Austria. The investigation was carried out on the machine tool AGIETRON Compact 1 from the company AGIE SA, Losone, Switzerland, for machining of through holes with a depth of t = 33.5 mm using different surface modified tool electrodes with a diameter of d = 0.8 mm. First results show a reduction of the erosion duration by 20 % for the application of thermally oxidized brass. The investigation of different defined oxide layer thicknesses and electrical resistances for specific applications are part of this ongoing work. This work is funded by the German Research Foundation (DFG). Copyright © Proceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020. All rights reserved.

关键词： Oxidation

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Experimental setup for the investigation of the transport of different gas bubble sizes during electrochemical machining based on similarity theory using particle image velocimetry

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Procedia CIRP 2022年 113卷 417-421页

作者： E. Tchoupe L. Heidemanns A. Klink T. Herrig M. Klaas D. Lauwers W. Schröder Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus-Boulevard 30 Aachen 52074 Germany Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Chair of Fluid Mechanics and Institute of Aerodynamics (AIA) RWTH Aachen University Wüllnerstr. 5a 52062 Aachen Germany

The major advantage of electrochemical machining (ECM) is the ability to machine materials regardless of their mechanical properties such as hardness or temperature resistance. This property of the ECM process makes it ideal for processing difficult-to-machine materials such as high-performance nickel-based alloys and single-crystal alloys. However, it is difficult to predict a priori the appropriate tool electrode for a given workpiece contour. This is not only due to the constantly changing properties of the electrolyte during machining, but also to the transient nature of the working gap. To simplify tool design in ECM, a better understanding of the various phenomena that occur, such as gas formation and its propagation in the working gap, is necessary. This paper presents an experimental investigation of the transport of different gas bubble sizes during ECM by up-scaling the working gap according to the principles of similarity theory. The multiphase flow is analyzed using 3D particle image velocimetry, which will serve for validation of future simulation models.

关键词： Electrochemical machining Particle Image Velocimetry Similarity approach

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Investigation of cutting mechanisms in the machining of Ceramic Matrix Composites (CMCs)

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Procedia CIRP 2021年 101卷 330-333页

作者： T. Bergs P. Ganser D. Fruh J. Reisberg Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Production Technology IPT Steinbachstrasse 17 52074 Aachen Germany

Ceramic Matrix Composites (CMCs) are increasingly used in numerous engineering fields, e.g. the aerospace, automotive, energy & nuclear industry due to their higher permissible material temperature and their lower weight compared to metallic materials. Thanks to the quasi-ductile material behaviour, it is possible to carry out time- and cost-efficient finishing with a geometrically defined cutting edge. In this study the cutting mechanisms are analysed in fundamental cutting tests on an orthogonal cutting test bench by variance of the undeformed chip thickness. For the evaluation cutting force measurements, high-speed recordings, surface qualities measurements and chip analyses were conducted.

关键词： Composites Machining Cutting

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The Concept of Digital Twin and Digital Shadow in Manufacturing

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Procedia CIRP 2021年 101卷 81-84页

作者： Thomas Bergs Sascha Gierlings Thomas Auerbach Andreas Klink Daniel Schraknepper Thorsten Augspurger Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Production Technology Steinbachstraße 17 52074 Aachen Germany

There still is a lacking comprehensive understanding of the relevant terms Digital Twin (DT) and Digital Shadow (DS) in particular within the application on the field of manufacturing technology. However, a common, transparent understanding of the concept and its requirements interferes with the implementation and application of these powerful tools. In this sense, the following publication gives a new definition of the concept within the field of manufacturing technology and shows specific applications.

关键词： Manufacturing Digital Twin Digital Shadow Process chain Process model

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Expert System for the machine Learning Pipeline in Manufacturing

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IFAC-PapersOnLine 2021年第1期54卷 128-133页

作者： M. Frye J. Krauß R.H. Schmitt Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 Aachen 52074 Germany Laboratory for Machine Tools WZL RWTH Aachen University Cluster Production Engineering 3A 540 Aachen 52074 Germany

Many success stories already exist with regard to the implementation of machine Learning (ML) and Artificial Intelligence (AI) in manufacturing. However, companies with traditional focus on production technologies face challenges in conducting AI-projects successfully and lack knowledge of which obstacles may occur and how to decide in the implementation phase. In this paper, we develop an approach that focuses on the methodological necessary steps for the successful application of ML and AI in manufacturing. Optimization potentials and decisions to be made are outlined in every step. A main focus is put on optimizing hyperparameters of ML-models as one promising approach for improving overall ML-model performance. An expert system is presented that enables the selection of suitable hyperparameter optimization techniques. The concept is validated based on manufacturing of compressor components of a turbofan engine.

关键词： machine Learning Artificial Intelligence Manufacturing Production Expert Systems Hyperparameter Optimization

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Development and Validation of Bone Models using Structural Dynamic Measurement Methods

Development and Validation of Bone Models using Structural D...

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作者： Neupetsch, Constanze Hensel, Eric Werner, Michael Meißner, Sven Troge, Jan Drossel, Welf-Guntram Rotsch, Christian Chemnitz University of Technology Professorship for Adaptronics and Lightweight Design in Production Chemnitz Germany Fraunhofer Institute for Machine Tools and Forming Technology IWU Chemnitz/Dresden Germany

Vibration measurement and signal analysis methods are common to evaluate the functionality and characteristics of technical components in different industrial and scientific areas. Modal analysis for example is a standard method to characterize the dynamic behavior of a structure and enables the development of validated bone models. The state of the art of analyzing bone structures does not include the modal damping, although it has a significant influence on the dynamic characteristics. Within the presented investigations, the modal analyses have been performed contactless with respect to excitation and response acquisition, which implies that there are no influences of shakers or sensor couplings. Therefore, an automatic impulse hammer and a 3D Scanning Laser Doppler Vibrometer were used for excitation and response detection. Various supports of the test specimens, surface pretreatments, excitation points and excitation impulses were examined to optimize the measurement setup and process. Experimental modal analysis data were analyzed by curve fitting methods to determine the modal parameters. To evaluate different structures and effects of damping, 3D printed artificial bones and animal in vitro bones were used to perform the measurements. To produce the cortical layer of the artificial bone models, volume models were generated based on medical image data and printed by polyamide-based selective laser sintering. The cancellous bone was represented by different foam fillings for the artificial bones. Thereby, the variation of the porosity was achieved by using different mixing ratios of polyurethane foam and hardener. Furthermore, the modal damping parameters were determined from the measurement of animal bones. The measurement time was optimized during the practical implementation of the parameter determination to minimize the influence of drying and decomposition processes on the measurement results. © 2019 by Walter de Gruyter Berlin/Boston.

关键词： Structural dynamics

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Benchmarking of Data Preprocessing Methods for machine Learning-Applications in Production

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Procedia CIRP 2021年 104卷 50-55页

作者： Maik Frye Johannes Mohren Robert H. Schmitt Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Laboratory for Machine Tools WZL RWTH Aachen University Cluster Production Engineering 3A 540 Aachen 52074 Germany

The application of machine learning (ML) is becoming increasingly common in production. However, many ML-projects in production fail due to poor data quality. To increase the quality, data needs to be preprocessed. Hundreds of methods exist for data preprocessing (DPP) that are selected manually depending on use-case requirements. For these reasons, DPP is currently performed unstructured and accounts for 80 % of ML-projects’ duration. Thus, we introduce a structured DPP-approach, in which DPP-methods are recommended based on production use-case requirements by benchmarking identified DPP-methods according to ML-model performance on five data sets. The approach is validated through two new use-cases.

关键词： Data Preprocessing Data Preparation machine Learning Artificial Intelligence Production Manufacturing

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Fundamental research of applying tungsten carbide-cobalt as tool electrode material for sinking EDM

Fundamental research of applying tungsten carbide-cobalt as ...

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20th CIRP Conference on Electro Physical and Chemical Machining, ISEM 2020

作者： Uhlmann, Eckart Polte, Mitchel Bolz, Robert Börnstein, Julian Fraunhofer Institute for Production Systems and Design Technology Pascalstrasse 8-9 Berlin10587 Germany Technische Universität Berlin Chair of Machine Tools and Manufacturing Technology Pascalstrasse 8-9 Berlin10587 Germany

The manufacturing process milling is limited in machining specific geometries like inner contours with sharp edges, high aspect ratios or defined radii. Sinking EDM (S-EDM) is an appropriate process to machine complex features if the negative geometry is machinable. The tool electrode wear is a critical factor, because it affects machining accuracy and process efficiency. The tool electrode wear depends on S-EDM processing parameters, the geometry and material of the tool electrode. The material tungsten carbide-cobalt owns suitable thermophysical properties for the application as tool electrode material in EDM. Most applications of tungsten carbide-cobalt in EDM are limited to EDM drilling processes, where tungsten carbide tube electrodes are widely used. The advancements in milling of hard materials enable an economic manufacturing of tungsten carbide-cobalt form electrodes for sinking EDM applications. However, it is not investigated which composition of the material tungsten carbide-cobalt is the most appropriate for the application as tool electrode material in S-EDM. Additionally, there are no suitable EDM process parameters in order to benefit from the material characteristics of the respective tungsten carbide-cobalt. Focus of this work is the analysis of different tungsten carbide grades regarding the average grain size and the cobalt content in order to identify correlations between the material characteristics and the EDM process results. Therefore, eight different tungsten carbide grades with four different grain sizes and five different cobalt contents were applied in S-EDM experimental analysis. The experimental studies showed a general suitability of tungsten carbide-cobalt tool electrodes for EDM-processing, where the material removal rate is comparable to commonly used tool electrode materials. Nevertheless, the relative tool wear shows inferior results. It could be observed that higher cobalt content and coarser grain size of the applied tungsten c

关键词： Tungsten carbide

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Automated machine learning for predictive quality in production 53

Automated machine learning for predictive quality in product...

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53rd CIRP Conference on Manufacturing Systems, CMS 2020

作者： Krauß, Jonathan Pacheco, Bruno Machado Zang, Hanno Maximilian Schmitt, Robert Heinrich Fraunhofer Institute for Production Technology IPT Steinbachstr. 17 Aachen52074 Germany Laboratory for Machine Tools WZL RWTH Aachen University Steinbachstr. 19 Aachen52074 Germany

Applications that leverage the benefits of applying machine learning (ML) in production have been successfully realized. A fundamental hurdle to scale ML-based projects is the necessity of expertise from manufacturing and data science. One possible solution lies in automating the ML pipeline: integration, preparation, modeling and model deployment. This paper shows the possibilities and limits of applying AutoML in production, including a benchmarking of available systems. Furthermore, AutoML is compared to manual implementation in a predictive quality use case: AutoML still requires programming knowledge and is outperformed by manual implementation - but sufficient results are available in a shorter timespan. © 2020 The Authors.

关键词： Benchmarking

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DECOMPOSITION OF INDUSTRIAL SYSTEMS FOR ENERGY EFFICIENCY OPTIMIZATION WITH OPTTOPO

arXiv

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arXiv 2022年

作者： Thiele, Gregor Johanni, Theresa Sommer, David Krüger, Jörg Department for Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology IPK Germany Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Institute for Machine Tools and Factory Management Technical University of Berlin Berlin Germany

The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system. © 2022, CC BY.

关键词： Optimization

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